Immunological bioinformatics

Using bioinformatics methods to generate a systems-level view of the immune system; description of the main biological concepts and the new data-driven algorithms.

Despite the fact that advanced bioinformatics methodologies have not been used as extensively in immunology as in other subdisciplines within biology, research in immunological bioinformatics has already developed models of components of the immune system that can be combined and that may help develop therapies, vaccines, and diagnostic tools for such diseases as AIDS, malaria, and cancer. In a broader perspective, specialized bioinformatics methods in immunology make possible for the first time a systems-level understanding of the immune system. The traditional approaches to immunology are reductionist, avoiding complexity but providing detailed knowledge of a single event, cell, or molecular entity. Today, a variety of experimental bioinformatics techniques connected to the sequencing of the human genome provides a sound scientific basis for a comprehensive description of the complex immunological processes. This book offers a description of bioinformatics techniques as they are applied to immunology, including a succinct account of the main biological concepts for students and researchers with backgrounds in mathematics, statistics, and computer science as well as explanations of the new data-driven algorithms in the context of biological data that will be useful for immunologists, biologists, and biochemists working on vaccine design. In each chapter the authors show interesting biological insights gained from the bioinformatics approach. The book concludes by explaining how all the methods presented in the book can be integrated to identify immunogenic regions in microorganisms and host genomes.

Author Notes

Ole Lund is Associate Professor and leader of the Immunological Bioinformatics group at the Center for Biological Sequence Analysis at Technical University of Denmark
Morten Nielsen and Claus Lundegaard are Associate Professors, and Soren Brunak is Professor and Center Director
Can Kesmir is Assistant Professor at the Department of Theoretical Biology at Utrecht University

Preface	p. ix
1 Immune Systems and Systems Biology	p. 1
1.1 Innate and Adaptive Immunity in Vertebrates	p. 10
1.2 Antigen Processing and Presentation	p. 11
1.3 Individualized Immune Reactivity	p. 14
2 Contemporary Challenges to the Immune System	p. 17
2.1 Infectious Diseases in the New Millennium	p. 17
2.2 Major Killers in the World	p. 17
2.3 Childhood Diseases	p. 21
2.4 Clustering of Infectious Disease Organisms	p. 22
2.5 Biodefense Targets	p. 24
2.6 Cancer	p. 30
2.7 Allergy	p. 31
2.8 Autoimmune Diseases	p. 32
3 Sequence Analysis in Immunology	p. 35
3.1 Sequence Analysis	p. 35
3.2 Alignments	p. 36
3.3 Multiple Alignments	p. 52
3.4 DNA Alignments	p. 54
3.5 Molecular Evolution and Phylogeny	p. 55
3.6 Viral Evolution and Escape: Sequence Variation	p. 57
3.7 Prediction of Functional Features of Biological Sequences	p. 61
4 Methods Applied in Immunological Bioinformatics	p. 69
4.1 Simple Motifs, Motifs and Matrices	p. 69
4.2 Information Carried by Immunogenic Sequences	p. 72
4.3 Sequence Weighting Methods	p. 75
4.4 Pseudocount Correction Methods	p. 77
4.5 Weight on Pseudocount Correction	p. 79
4.6 Position Specific Weighting	p. 79
4.7 Gibbs Sampling	p. 80
4.8 Hidden Markov Models	p. 84
4.9 Artificial Neural Networks	p. 91
4.10 Performance Measures for Prediction Methods	p. 99
4.11 Clustering and Generation of Representative Sets	p. 102
5 DNA Microarrays in Immunology	p. 103
5.1 DNA Microarray Analysis	p. 103
5.2 Clustering	p. 106
5.3 Immunological Applications	p. 108
6 Prediction of Cytotoxic T Cell (MHC Class I) Epitopes	p. 111
6.1 Background and Historical Overview of Methods for Peptide MHC Binding Prediction	p. 112
6.2 MHC Class I Epitope Binding Prediction Trained on Small Data Sets	p. 114
6.3 Prediction of CTL Epitopes by Neural Network Methods	p. 120
6.4 Summary of the Prediction Approach	p. 133
7 Antigen Processing in the MHC Class I Pathway	p. 135
7.1 The Proteasome	p. 135
7.2 Evolution of the Immunosubunits	p. 137
7.3 Specificity of the (Immuno)Proteasome	p. 139
7.4 Predicting Proteasome Specificity	p. 143
7.5 Comparison of Proteasomal Prediction Performance	p. 147
7.6 Escape from Proteasomal Cleavage	p. 149
7.7 Post-Proteasomal Processing of Epitopes	p. 150
7.8 Predicting the Specificity of TAP	p. 153
7.9 Proteasome and TAP Evolution	p. 154
8 Prediction of Helper T Cell (MHC Class II) Epitopes	p. 157
8.1 Prediction Methods	p. 158
8.2 The Gibbs Sampler Method	p. 159
8.3 Further Improvements of the Approach	p. 172
9 Processing of MHC Class II Epitopes	p. 175
9.1 Enzymes Involved in Generating MHC Class II Ligands	p. 176
9.2 Selective Loading of Peptides to MHC Class II Molecules	p. 179
9.3 Phylogenetic Analysis of the Lysosomal Proteases	p. 180
9.4 Signs of the Specificities of Lysosomal Proteases on MHC Class II Epitopes	p. 182
9.5 Predicting the Specificity of Lysosomal Enzymes	p. 182
10 B Cell Epitopes	p. 187
10.1 Affinity Maturation	p. 188
10.2 Recognition of Antigen by B cells	p. 191
10.3 Neutralizing Antibodies	p. 201
11 Vaccine Design	p. 203
11.1 Categories of Vaccines	p. 204
11.2 Polytope Vaccine: Optimizing Plasmid Design	p. 207
11.3 Therapeutic Vaccines	p. 209
11.4 Vaccine Market	p. 213
12 Web-Based Tools for Vaccine Design	p. 215
12.1 Databases of MHC Ligands	p. 215
12.2 Prediction Servers	p. 217
13 MHC Polymorphism	p. 223
13.1 What Causes MHC Polymorphism?	p. 223
13.2 MHC Supertypes	p. 225
14 Predicting Immunogenicity: An Integrative Approach	p. 243
14.1 Combination of MHC and Proteasome Predictions	p. 244
14.2 Independent Contributions from TAP and Proteasome Predictions	p. 245
14.3 Combinations of MHC, TAP, and Proteasome Predictions	p. 247
14.4 Validation on HIV Data Set	p. 251
14.5 Perspectives on Data Integration	p. 252
References	p. 254
Index	p. 291

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Table of Contents